{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import ssms\n",
    "from matplotlib import pyplot as plt\n",
    "from ssms.basic_simulators import simulator\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on package ssms:\n",
      "\n",
      "NAME\n",
      "    ssms - # import importlib.metadata\n",
      "\n",
      "PACKAGE CONTENTS\n",
      "    basic_simulators (package)\n",
      "    config (package)\n",
      "    dataset_generators (package)\n",
      "    support_utils (package)\n",
      "\n",
      "DATA\n",
      "    __all__ = ['basic_simulators', 'dataset_generators', 'config', 'suppor...\n",
      "\n",
      "VERSION\n",
      "    0.6.0\n",
      "\n",
      "FILE\n",
      "    /Users/afengler/Library/CloudStorage/OneDrive-Personal/proj_ssm_simulators/ssm-simulators/ssms/__init__.py\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ssms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'ddm_mic2_ornstein',\n",
       " 'params': ['vh', 'vl1', 'vl2', 'a', 'zh', 'zl1', 'zl2', 'd', 'g', 't'],\n",
       " 'param_bounds': [[-4.0, -4.0, -4.0, 0.3, 0.2, 0.2, 0.2, 0.0, 0.0, 0.0],\n",
       "  [4.0, 4.0, 4.0, 2.5, 0.8, 0.8, 0.8, 1.0, 3.0, 2.0]],\n",
       " 'boundary': <function ssms.basic_simulators.boundary_functions.constant(t=0)>,\n",
       " 'n_params': 10,\n",
       " 'default_params': [0.0, 0.0, 0.0, 1.0, 0.5, 0.5, 0.5, 0.5, 1.5, 0.5],\n",
       " 'hddm_include': ['vh', 'vl1', 'vl2', 'a', 'zh', 'zl1', 'zl2', 'd', 'g', 't'],\n",
       " 'nchoices': 4}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ssms.config.model_config[\"ddm_mic2_ornstein\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = ssms.basic_simulators.simulator.simulator(\n",
    "    model=\"ddm_mic2_ornstein\",\n",
    "    theta={\n",
    "        \"vh\": 1.0,\n",
    "        \"vl1\": 2.0,\n",
    "        \"vl2\": 2.0,\n",
    "        \"a\": 1.5,\n",
    "        \"zh\": 0.5,\n",
    "        \"zl1\": 0.5,\n",
    "        \"zl2\": 0.5,\n",
    "        \"d\": 0.0,\n",
    "        \"g\": 0.0,\n",
    "        \"t\": 0.5,\n",
    "    },\n",
    "    n_samples=10000,\n",
    "    delta_t=0.001,\n",
    "    no_noise=False,\n",
    "    smooth_unif=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['rts', 'choices', 'rts_high', 'rts_low', 'metadata'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'vh': array([1.], dtype=float32),\n",
       " 'vl1': array([2.], dtype=float32),\n",
       " 'vl2': array([2.], dtype=float32),\n",
       " 'a': array([1.5], dtype=float32),\n",
       " 'zh': array([0.5], dtype=float32),\n",
       " 'zl1': array([0.5], dtype=float32),\n",
       " 'zl2': array([0.5], dtype=float32),\n",
       " 'd': array([0.], dtype=float32),\n",
       " 't': array([0.5], dtype=float32),\n",
       " 's': 1.0,\n",
       " 'delta_t': 0.0010000000474974513,\n",
       " 'max_t': 20.0,\n",
       " 'n_samples': 10000,\n",
       " 'simulator': 'ddm_flexbound_mic2_adj',\n",
       " 'boundary_fun_type': 'constant',\n",
       " 'possible_choices': [0, 1, 2, 3],\n",
       " 'trajectory': 'This simulator does not yet allow for trajectory simulation',\n",
       " 'boundary': array([1.5, 1.5, 1.5, ..., 1.5, 1.5, 1.5], dtype=float32),\n",
       " 'model': 'ddm_mic2_ornstein'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[\"metadata\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x16e3832e0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = {0: \"blue\", 1: \"green\", 2: \"yellow\", 3: \"red\", 4: \"black\"}\n",
    "labels = {0: \"WW\", 1: \"WR\", 2: \"RW\", 3: \"RR\"}\n",
    "for choice in x[\"metadata\"][\"possible_choices\"]:\n",
    "    plt.hist(\n",
    "        x[\"rts\"][x[\"choices\"] == choice],\n",
    "        alpha=0.5,\n",
    "        label=labels[choice],\n",
    "        color=colors[choice],\n",
    "        histtype=\"step\",\n",
    "        bins=100,\n",
    "    )\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratio_list = []\n",
    "mean_rt_list = []\n",
    "for g_tmp in np.linspace(0, 10, 10):\n",
    "    ratio_list.append([])\n",
    "    mean_rt_list.append([])\n",
    "    for v_low in np.linspace(0, 2, 10):\n",
    "        x = ssms.basic_simulators.simulator.simulator(\n",
    "            model=\"ddm_mic2_ornstein\",\n",
    "            theta={\n",
    "                \"vh\": 1.0,\n",
    "                \"vl1\": v_low,\n",
    "                \"vl2\": v_low,\n",
    "                \"a\": 1.5,\n",
    "                \"zh\": 0.5,\n",
    "                \"zl1\": 0.5,\n",
    "                \"zl2\": 0.5,\n",
    "                \"d\": 0.0,\n",
    "                \"g\": g_tmp,\n",
    "                \"t\": 0.5,\n",
    "            },\n",
    "            n_samples=10000,\n",
    "            delta_t=0.001,\n",
    "            no_noise=False,\n",
    "            smooth_unif=False,\n",
    "        )\n",
    "        ratio_list[-1].append(\n",
    "            np.sum(x[\"rts_low\"] < x[\"rts_high\"]) / x[\"rts_low\"].shape[0]\n",
    "        )\n",
    "        mean_rt_list[-1].append(np.mean(x[\"rts\"]))\n",
    "\n",
    "ratio_list_precise = []\n",
    "mean_rt_list_precise = []\n",
    "for g_tmp in np.linspace(0, 10, 10):\n",
    "    ratio_list_precise.append([])\n",
    "    mean_rt_list_precise.append([])\n",
    "    for v_low in np.linspace(0, 2, 10):\n",
    "        x = ssms.basic_simulators.simulator.simulator(\n",
    "            model=\"ddm_mic2_ornstein\",\n",
    "            theta={\n",
    "                \"vh\": 1.0,\n",
    "                \"vl1\": v_low,\n",
    "                \"vl2\": v_low,\n",
    "                \"a\": 1.5,\n",
    "                \"zh\": 0.5,\n",
    "                \"zl1\": 0.5,\n",
    "                \"zl2\": 0.5,\n",
    "                \"d\": 0.0,\n",
    "                \"g\": g_tmp,\n",
    "                \"t\": 0.5,\n",
    "            },\n",
    "            n_samples=10000,\n",
    "            delta_t=0.0001,\n",
    "            no_noise=False,\n",
    "            smooth_unif=False,\n",
    "        )\n",
    "        ratio_list_precise[-1].append(\n",
    "            np.sum(x[\"rts_low\"] < x[\"rts_high\"]) / x[\"rts_low\"].shape[0]\n",
    "        )\n",
    "        mean_rt_list_precise[-1].append(np.mean(x[\"rts\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.3444, 0.3381, 0.3596, 0.371, 0.3958, 0.438, 0.4545, 0.4992, 0.5304, 0.568],\n",
       " [0.1361,\n",
       "  0.1362,\n",
       "  0.1437,\n",
       "  0.1575,\n",
       "  0.1851,\n",
       "  0.2134,\n",
       "  0.2488,\n",
       "  0.2824,\n",
       "  0.3371,\n",
       "  0.379],\n",
       " [0.0315,\n",
       "  0.0303,\n",
       "  0.0354,\n",
       "  0.0397,\n",
       "  0.0598,\n",
       "  0.0705,\n",
       "  0.0816,\n",
       "  0.1036,\n",
       "  0.1265,\n",
       "  0.1603],\n",
       " [0.0036,\n",
       "  0.0048,\n",
       "  0.0039,\n",
       "  0.007,\n",
       "  0.0084,\n",
       "  0.011,\n",
       "  0.0191,\n",
       "  0.0247,\n",
       "  0.0304,\n",
       "  0.0437],\n",
       " [0.0003,\n",
       "  0.0008,\n",
       "  0.0005,\n",
       "  0.0008,\n",
       "  0.0009,\n",
       "  0.0018,\n",
       "  0.003,\n",
       "  0.0047,\n",
       "  0.0039,\n",
       "  0.0065],\n",
       " [0.0002, 0.0, 0.0, 0.0002, 0.0001, 0.0005, 0.0005, 0.0006, 0.0011, 0.0006],\n",
       " [0.0, 0.0001, 0.0, 0.0001, 0.0, 0.0, 0.0, 0.0, 0.0001, 0.0],\n",
       " [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n",
       " [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],\n",
       " [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratio_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnt = 0\n",
    "for ratio_list_tmp in ratio_list:\n",
    "    plt.scatter(\n",
    "        np.linspace(0, 2, 10), ratio_list_tmp, alpha=cnt / len(ratio_list), color=\"blue\"\n",
    "    )\n",
    "    cnt += 1\n",
    "\n",
    "cnt = 0\n",
    "for ratio_list_precise_tmp in ratio_list:\n",
    "    plt.scatter(\n",
    "        np.linspace(0, 2, 10),\n",
    "        ratio_list_precise_tmp,\n",
    "        alpha=cnt / len(ratio_list),\n",
    "        color=\"red\",\n",
    "        marker=\"x\",\n",
    "    )\n",
    "    cnt += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnt = 0\n",
    "for mean_rt_list_tmp in mean_rt_list:\n",
    "    plt.scatter(\n",
    "        np.linspace(0, 2, 10),\n",
    "        mean_rt_list_tmp,\n",
    "        alpha=cnt / len(ratio_list),\n",
    "        color=\"blue\",\n",
    "    )\n",
    "    cnt += 1\n",
    "\n",
    "cnt = 0\n",
    "for mean_rt_list_precise_tmp in mean_rt_list_precise:\n",
    "    plt.scatter(\n",
    "        np.linspace(0, 2, 10) + 0.1,\n",
    "        mean_rt_list_precise_tmp,\n",
    "        alpha=cnt / len(ratio_list),\n",
    "        color=\"red\",\n",
    "        marker=\"x\",\n",
    "    )\n",
    "    cnt += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.22222222, 0.44444444, 0.66666667, 0.88888889,\n",
       "       1.11111111, 1.33333333, 1.55555556, 1.77777778, 2.        ])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0, 2, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5582"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(x[\"rts_low\"] < x[\"rts_high\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['rts', 'choices', 'rt_high', 'rts_low', 'metadata'])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_rt_list = []\n",
    "for v_low in np.linspace(0, 0.1, 10):\n",
    "    x = ssms.basic_simulators.simulator.simulator(\n",
    "        model=\"ddm\",\n",
    "        theta={\"v\": v_low, \"a\": 1.0, \"z\": 0.5, \"t\": 0.5},\n",
    "        n_samples=100000,\n",
    "        delta_t=0.002,\n",
    "        no_noise=False,\n",
    "        smooth_unif=True,\n",
    "    )\n",
    "    mean_rt_list.append(np.mean(x[\"rts\"]))\n",
    "\n",
    "mean_rt_list_precise = []\n",
    "for v_low in np.linspace(0, 0.1, 10):\n",
    "    x_prec = ssms.basic_simulators.simulator.simulator(\n",
    "        model=\"ddm\",\n",
    "        theta={\"v\": v_low, \"a\": 1.0, \"z\": 0.5, \"t\": 0.5},\n",
    "        n_samples=100000,\n",
    "        delta_t=0.001,\n",
    "        no_noise=False,\n",
    "        smooth_unif=True,\n",
    "    )\n",
    "    mean_rt_list_precise.append(np.mean(x_prec[\"rts\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2962cff10>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(np.linspace(0, 2, 10), mean_rt_list, color=\"blue\")\n",
    "plt.scatter(np.linspace(0, 2, 10) + 0.1, mean_rt_list_precise, color=\"red\", marker=\"x\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([3.000e+00, 0.000e+00, 0.000e+00, 1.000e+00, 1.000e+00, 1.000e+00,\n",
       "        2.000e+00, 1.000e+00, 1.000e+00, 2.000e+00, 0.000e+00, 5.000e+00,\n",
       "        6.000e+00, 1.100e+01, 1.800e+01, 1.900e+01, 2.000e+01, 1.800e+01,\n",
       "        3.300e+01, 4.300e+01, 5.600e+01, 6.800e+01, 8.700e+01, 1.110e+02,\n",
       "        1.550e+02, 2.000e+02, 2.620e+02, 3.390e+02, 3.980e+02, 5.690e+02,\n",
       "        7.050e+02, 8.510e+02, 1.167e+03, 1.463e+03, 1.910e+03, 2.438e+03,\n",
       "        3.105e+03, 3.978e+03, 5.202e+03, 6.741e+03, 8.129e+03, 6.571e+03,\n",
       "        3.030e+02, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 9.420e+02,\n",
       "        8.992e+03, 9.754e+03, 7.828e+03, 6.200e+03, 4.764e+03, 3.787e+03,\n",
       "        2.853e+03, 2.301e+03, 1.703e+03, 1.310e+03, 1.048e+03, 7.590e+02,\n",
       "        6.080e+02, 5.060e+02, 3.660e+02, 2.790e+02, 2.070e+02, 1.610e+02,\n",
       "        1.380e+02, 1.180e+02, 9.000e+01, 8.200e+01, 4.700e+01, 4.100e+01,\n",
       "        3.200e+01, 1.500e+01, 1.800e+01, 1.700e+01, 1.100e+01, 1.000e+01,\n",
       "        6.000e+00, 1.000e+00, 4.000e+00, 1.000e+00, 1.000e+00, 1.000e+00,\n",
       "        3.000e+00, 0.000e+00, 0.000e+00, 1.000e+00, 0.000e+00, 0.000e+00,\n",
       "        1.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 0.000e+00,\n",
       "        0.000e+00, 0.000e+00, 0.000e+00, 1.000e+00]),\n",
       " array([-9.51421738, -9.30247316, -9.09072895, -8.87898474, -8.66724052,\n",
       "        -8.45549631, -8.2437521 , -8.03200788, -7.82026367, -7.60851946,\n",
       "        -7.39677525, -7.18503103, -6.97328682, -6.76154261, -6.54979839,\n",
       "        -6.33805418, -6.12630997, -5.91456575, -5.70282154, -5.49107733,\n",
       "        -5.27933311, -5.0675889 , -4.85584469, -4.64410048, -4.43235626,\n",
       "        -4.22061205, -4.00886784, -3.79712362, -3.58537941, -3.3736352 ,\n",
       "        -3.16189098, -2.95014677, -2.73840256, -2.52665834, -2.31491413,\n",
       "        -2.10316992, -1.8914257 , -1.67968149, -1.46793728, -1.25619307,\n",
       "        -1.04444885, -0.83270464, -0.62096043, -0.40921621, -0.197472  ,\n",
       "         0.01427221,  0.22601643,  0.43776064,  0.64950485,  0.86124907,\n",
       "         1.07299328,  1.28473749,  1.4964817 ,  1.70822592,  1.91997013,\n",
       "         2.13171434,  2.34345856,  2.55520277,  2.76694698,  2.9786912 ,\n",
       "         3.19043541,  3.40217962,  3.61392384,  3.82566805,  4.03741226,\n",
       "         4.24915648,  4.46090069,  4.6726449 ,  4.88438911,  5.09613333,\n",
       "         5.30787754,  5.51962175,  5.73136597,  5.94311018,  6.15485439,\n",
       "         6.36659861,  6.57834282,  6.79008703,  7.00183125,  7.21357546,\n",
       "         7.42531967,  7.63706388,  7.8488081 ,  8.06055231,  8.27229652,\n",
       "         8.48404074,  8.69578495,  8.90752916,  9.11927338,  9.33101759,\n",
       "         9.5427618 ,  9.75450602,  9.96625023, 10.17799444, 10.38973866,\n",
       "        10.60148287, 10.81322708, 11.02497129, 11.23671551, 11.44845972,\n",
       "        11.66020393]),\n",
       " [<matplotlib.patches.Polygon at 0x2961b1fc0>])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x[\"rts\"] * x[\"choices\"], bins=100, alpha=0.5, histtype=\"step\", color=\"blue\")\n",
    "plt.hist(\n",
    "    x_prec[\"rts\"] * x_prec[\"choices\"], bins=100, alpha=0.5, histtype=\"step\", color=\"red\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ssms_dev",
   "language": "python",
   "name": "ssms_dev"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
